Métissage methodology for qualitative research
Bibliographic record
Abstract
There is a lack of Métis-specific research methodologies that can be drawn upon in academia by Métis scholars and emerging academics like master and Ph.D. students. This paper outlines an exploratory research methodology used by a Ph.D. student in educational studies for a qualitative study on reconciliation through Métissage in higher education. The study was designed to build a conceptual framework[1] that answers the following question: “How have university courses and learning experiences impacted Métis peoples’ understandings of their cultural identities, the role of Métis-specific Knowledge in higher education curriculum and policies, and Métis perspectives on reconciliation in Canadian universities?” This exploratory methodology allows a deeper understanding of Métis peoples’ experiences in university classrooms. Like the mixed worldview of the Métis people, a mix of grounded theory and Indigenous-Métissage methodology has provided an innovative way for one Métis Ph.D. candidate to attempt to ground their research within their culture. In particular, the application of grounded theory and the sharing of stories through the conversational method is used in this study. The use of narrative also contributes to a rich set of methods allowing for auto-ethnography to be woven throughout the research methodology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".